The modern communications era has brought about a tremendous expansion of wireline and wireless networks. Computer networks, television networks, and telephony networks are experiencing an unprecedented technological expansion, fueled by consumer demand. Wireless and mobile networking technologies have addressed related consumer demands, while providing more flexibility and immediacy of information transfer.
Current and future networking technologies continue to facilitate ease of information transfer and convenience to users. One area in which there is a demand to increase the ease of information transfer and convenience to users relates to provision of information retrieval in networks. For example, information such as audio, video, image content, text, data, etc., may be made available for retrieval between different entities using various communication networks. Accordingly, devices associated with each of the different entities may be placed in communication with each other to locate and affect a transfer of the information.
In certain situations, for example, when a user wishes to retrieve image content from a particular location such as a database, the user may wish to review images based on their content. In this regard, for example, the user may wish to review images of cats, animals, cars, etc. Although some mechanisms have been provided by which metadata may be associated with content items to enable a search for content based on the metadata, insertion of such metadata may be time consuming. Additionally, a user may wish to find content in a database in which the use of metadata is incomplete or unreliable. Accordingly, content based image retrieval (CBIR) solutions have been developed which utilize, for example, a support vector machine (SVM) to classify content based on its relevance with respect to a particular query. Thus, for example, if a user desires to search a database for images of cats, a query image could be provided of a cat and the SVM could search through the database and provide images to the user based on their relevance with respect to the features of the query image.
However, CBIR often classifies images based on low-level features such as color, shape, texture, etc. Accordingly, the boundary between relevance and irrelevance may not be highly refined. In an effort to improve CBIR performance, the concept of relevance feedback was developed. Relevance feedback relates to providing feedback to the SVM regarding images presented as to the relevance of the images. The assumption is that given the relevance feedback, the SVM may better learn the classification boundary between relevant and irrelevant images. However, providing relevance feedback can also become a tedious operation if too much feedback is required to develop an effective classification boundary.
Accordingly, it may be advantageous to provide an improved method of providing relevance feedback, which may overcome the disadvantages described above.